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Automated security scan — not a certification.
Coco Super Intelligence: agent skills, slash commands and subagents for Claude Code, Cursor and Codex. One command installs them into the tool you already use.
# Add to your Claude Code skills
git clone https://github.com/coco-research/cocoGuides for using ai agents skills like coco.
Last scanned: 9/24/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-09-24T09:01:11.487Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how coco compares with popular alternatives.
coco is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by coco-research. Coco Super Intelligence: agent skills, slash commands and subagents for Claude Code, Cursor and Codex. One command installs them into the tool you already use. It has 628 GitHub stars.
Yes. coco passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/coco-research/coco" and add it to your Claude Code skills directory (see the Installation section above).
coco is primarily written in Python. It is open-source under coco-research on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh coco against similar tools.
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Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
495 personas across 13 departments, 229 skills[^installed] and 388 commands in the full catalog (coco 1.9.0, 2026-10-21).
Agent skills, slash commands and subagents for Claude Code, Cursor, Codex and other AI coding agents. One command installs them into the tool you already use. Coco's files and state stay on your machine. Your coding agent still uses its own model provider.
npx cocosuperintelligence
Open-core: the core is Apache-2.0, and Super Intelligence is proprietary. Relicensed from MIT to Apache-2.0 from 2026-11-05. Earlier releases remain under MIT. Files stay on your machine. No telemetry. A version check may contact GitHub once a day.
A hard call in a coding session usually gets one model's opinion, with no name on it and no record of who disagreed. CoCo seats a cross-team board of 495 personas, each modeled on the public writing of a named expert, answering from cited public stances, and returns one pick plus the dissent. They are not those people's views or endorsement. The same install carries the skills and commands that do the work after the verdict, and writes phase state to disk so a cleared chat does not erase the plan. It is not a new model and not a new harness. It installs into the one you already run.
Personas are modeled on the public writing of named experts and are not those people's views or endorsement. See the persona disclaimer. To request removal or correction of a persona, see docs/personas/TAKEDOWN.md.
An illustration built from a real /SI-Decide run (Claude Sonnet 5.5, 30 September 2026, US$0.41). Persona names are replaced with role archetypes and the output is trimmed for length; no other words are changed. The full output, with the same replacements, is in docs/readme/terminal/si-decide-full.md. Note what it admits: the embedding router was offline, so it fell back to keywords, and stances applied beyond their source are marked extrapolated.
A real meta_select.py Stage-A run on this machine. The embedding router was offline (Connection refused), so this shows the keyword-fallback routing path (method: keyword, degraded: true), not embed cosine.
Prerequisites: git, bash, Node.js 14 or newer (for npx), and python3 (it generates the /SI-* commands).
npx cocosuperintelligence installs the latest release. It clones the pinned release tag (currently v1.10.0, not floating main) into ./coco of the current directory, then runs install.sh. Run it from your home folder, so the clone is ~/coco and not a folder inside another project. On a fresh machine it took about 3 minutes, including the download.
npx cocosuperintelligence
Open Claude Code and run:
/SI-Decide "Should a 10-person startup move its public REST API to GraphQL this quarter?"
Newest main, which is ahead of that pin:
git clone https://github.com/coco-research/coco.git ~/coco && bash ~/coco/install.sh
Adapter flags and bundle selection: docs/install.md.
/SI-Decide seats 16-32 of 495 personas across 13 departments. Add --debate for reaction rounds./clear. Decisions, phase state, and progress stay on disk.COCO_NO_UPDATE_CHECK=1 to skip the daily GitHub version check.You type a question. The orchestrator scores 495 personas across 13 departments on domain fit, cell coverage, and productive conflict, then seats 16-32. Each seated persona is modeled on the public writing of a named expert, answers from cited public stances, and is not that person's views or endorsement. --debate adds reaction rounds before the tally. The decision is one pick, the dissents are named, and a stance check flags anything that does not trace to a source.
How the router scores teams: systems/superintelligence/README.md. What each editor actually receives: adapters/INDEX.md.
Released on npm: npx cocosuperintelligence installs the latest release (CHANGELOG).
Measured on main, a default Claude Code install delivers 191 of the 229 skills (the rest are the opt-in security pack and Cursor-only skills), 388 commands and 34 agents (docs/delivered-counts.json).
Known gap: most GSD skills need the upstream GSD toolkit at ~/.claude/get-shit-done (see systems/gsd/README.md).
From a checkout you already have, bash install.sh auto-detects the editor. Narrow it with --adapter cursor or --systems gsd,brain. --core-only skips bundles. reverse-skill stays opt-in: --systems reverse-skill.
The remote bootstrap clones the same pinned release tag as npx (not floating main) to ~/.coco, prints the commit, and waits for you to type y before it runs install.sh:
bash <(curl -fsSL https://raw.githubusercontent.com/coco-research/coco/main/bin/coco-bootstrap.sh)
npx cocosuperintelligence update checks out the pinned release tag. A git clone updates with git pull --ff-only && bash install.sh. bash scripts/check-update.sh prints the version. The check contacts GitHub at most once a day and sends no telemetry.
Uninstall removes symlinks whose target is this clone, and only those. Run it from the clone root:
CLONE="$(pwd)"
find ~/.claude ~/.cursor ~/.grok ~/.copilot -type l -lname "${CLONE}/*" -delete
for app in "Code" "Code - Insiders" "VSCodium" "VSCodium - Insiders"; do for base in "$HOME/Library/Application Support" "${XDG_CONFIG_HOME:-$HOME/.config}"; do [ -d "$base/$app/User" ] && find "$base/$app/User" -type l -lname "${CLONE}/*" -delete; done; done
A contains-match on the folder name would also delete links into a path that merely contains that name (for example coco-research when the clone is coco). Hermes profiles live under ~/.hermes/profiles; use the same "${CLONE}/*" prefix there. Uninstall does not remove the generated SI*.md command files or the rules block between <!-- coco:rules-start --> and <!-- coco:rules-end --> in CLAUDE.md; delete those by hand.
Super Intelligence summons a panel inside the session you already have open. Every contribution is from a persona modeled on the public writing of a named expert and grounded in a cited public source. It is not that person's views or endorsement.
/SI-Decide "Should we migrate our database to pgvector?"
/SI-AI-Decide "Which embedding model gives the best cost/performance?"
/SI-Eng-Pre-Mortem "Review our zero-downtime cache migration strategy"
/SI-GRC-Review "Check this customer onboarding flow for GDPR compliance"
495 expert personas, organized across 13 specialized departments:
| Department | Experts | Focus |
|---|---|---|
| Engineering | 70 | Software architecture, cloud systems, systems coding, compilers, infrastructure |
| AI | 59 | Neural-network research, LLM optimization, safety, alignment, vector databases |
| Product & Design | 56 | Design systems, UX, product strategy, growth loops |
| Finance | 47 | Valuation, corporate finance, macro modeling, fintech infrastructure |
| Trading | 46 | Quant analysis, market microstructure, derivatives pr |